Chris J. Myers
University of Colorado Boulder
241 Papers
1.2K Citations
Chris J. Myers is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Computer science & Formal verification. The author has an hindex of 37, co-authored 223 publications. Previous affiliations of Chris J. Myers include National Institute of Informatics & Utah State University.
Chat about Author
Papers
Sequence-Based Searching for SynBioHub Using VSEARCH.
TL;DR: A new approach to scoring part similarity using SBOLExplorer is presented, which takes into account both the popularity and percentage match of parts.
5
Curation Principles Derived from the Analysis of the SBOL iGEM Data Set.
Jeanet Mante,Nicholas Roehner,Kevin W Keating,James Alastair McLaughlin,Eric M. Young,Jacob Beal,Chris J. Myers +6 more
TL;DR: In this paper, a curated library of parts from the International Genetically Engineered Machines (iGEM) registry data set is presented, which can be used to facilitate the creation and curation of other part libraries using a simpler and less labor intensive process.
5
A Fault-Tolerant Routing Algorithm for a Network-on-Chip Using a Link Fault Model
Jian Wu,Zhen Zhang,Chris J. Myers +2 more
- 01 Jan 2011
TL;DR: An improved routing algorithm based on the Glass/Ni protocol which tolerates a single link fault while still avoiding deadlock in a mesh network is proposed and Simulation results indicate that this improved algorithm provides significant improvements in network reliability with minimal cost.
5
Direct synthesis of timed asynchronous circuits
Sung Tae Jung,Chris J. Myers +1 more
- 07 Nov 1999
TL;DR: This paper shows that a timed circuit-not containing circuit hazards under given timing constraints-can be found by using the relations between signal transitions of the specification, and can be efficiently found using a heuristic timing analysis algorithm.
5
•Posted Content
Approximation Techniques for Stochastic Analysis of Biological Systems
TL;DR: This chapter addresses the scalability problem by presenting a state-space approximation method to remove unlikely states resulting in a reduced, finite state representation of the infinite-state continuous-time Markov chain that is amenable to probabilistic model checking.
5